## Structural Equation Modeling Using SAS
This course introduces the experienced statistical analyst to structural equation modeling (SEM) and the new PATH language in the CALIS procedure in SAS/STAT software. The course also includes a separate e-Learning course that introduces the SAS Structural Equation Modeling for JMP interface for performing analysis of structural equation models with an easy-to-use diagram-creating interface. Structural equation modeling is a statistical technique that combines elements of traditional multivariate models, such as regression analysis, factor analysis, and simultaneous equation modeling. These models are often represented as matrices, equations, and/or path diagrams and can explicitly account for uncertainty in observed variables and for estimation bias due to measurement error. Competing models can be compared to one another, providing information about the complex drivers of the outcome variables of interest. Many applications of SEM can be found in the social, economic, and behavioral sciences, where measu
- explain a regression model in terms of a structural equation model
- compare results from the REG and CALIS procedures
- specify models and evaluate model fit in the CALIS procedure using the PATH input style
- specify mediation models and test for complete and partial mediation
- perform complex path analysis
- perform confirmatory factor analysis
- specify general latent variable models
- perform full-information maximum likelihood estimation for incomplete data
- perform honest assessment to validate models
- draw path diagrams in the JMP SEM interface to specify a model (self-study).
Before attending this course, you should - have a strong background in regression modeling
- be familiar with factor analysis
- be familiar with the concepts taught in Statistics 2: ANOVA and Regression or have equivalent knowledge.
This course addresses SAS/STAT software.
- introduction
- simple linear regression
- terminology
The CALIS Procedure - introduction
- PATH input
Path Models - path models overview
- mediation models
- assessment of model fit
- model validation
- covariance matrices as input
An Introduction to Latent Variable Models - confirmatory factor analysis
- general latent variable models
Additional Topics - handling missing data
- nonnormal data
- further study
A Point-and-Click Approach to Specifying Structural Equation Models -Self Study - overview of SAS structural equation modeling for JMP
- analysis using SAS structural equation modeling for JMP
| United States OFFERS FOR THIS COURSE |